Human Protein Cluster Analysis Using Amino Acid Frequencies

Abstract

The paper focuses on the development of a software tool for protein clustering according to their amino acid content. All known human proteins were clustered according to the relative frequencies of their amino acids starting from the UniProtKB/Swiss-Prot reference database and making use of hierarchical cluster analysis. Results were compared to those based on sequence similarities. Results: Proteins display different clustering patterns according to type. Many extracellular proteins with highly specific and repetitive sequences (keratins, collagens etc.) cluster clearly confirming the accuracy of the clustering method. In our case clustering by sequence and amino acid content overlaps. Proteins with a more complex structure with multiple domains (catalytic, extracellular, transmembrane etc.), even if classified very similar according to sequence similarity and function (aquaporins, cadherins, steroid 5-alpha reductase etc.) showed different clustering according to amino acid content. Availability of essential amino acids according to local conditions (starvation, low or high oxygen, cell cycle phase etc.) may be a limiting factor in protein synthesis, whatever the mRNA level. This type of protein clustering may therefore prove a valuable tool in identifying so far unknown metabolic connections and constraints.

The analysis of glutamate and glutamine frequencies in human proteins as marker of tissue oxygenation

“…The protein sequences in FLAT file format were downloaded from UniProtKB/Swiss-Prot protein database (http://www.uniprot.org/uniprot/). A software tool [2] was applied to the data contained in the FLAT file of the protein. The SQ section of the FLAT file was used to compute the absolute and the relative frequencies of all AAs contained in the protein.…”

“…In our previous study [2], we found several evidences that the composition of proteins gives information on AA availability and local control of protein expression. The study compared human proteins on the basis of their AA content by a hierarchical, agglomerative clustering method.…”

Beyond mutations: Accounting for quantitative changes in the analysis of protein evolution

“…Substitution models of evolution describe the process of genetic variation through fixed mutations and constitute a basis of the evolutionary analysis on the molecular level [2] . Yet, the focus on the manifestation of amino acid changes (or amino acid frequencies [29] does not account for the circumstance that selection and self-organization favor certain mutations over others (natural selection could have acted in sequence space to select non-random sequences satisfying the requirement for a large energy gap between native and unfolded conformations [23] . Notably, such accounting has become principally feasible over the past years, in which a substantial body of knowledge has been accrued to describe the physical and chemical properties of amino acids.…”

Visual Multi-Metric Grouping of Eye-Tracking Data

“…Understanding participants' behavioral patterns and learning about commonalities and diversities in these is a challenging task. These types of analyses were done in past for many fields, such as usability research using machine learning, data mining (Paliouras, Papatheodorou, Karkaletsis, & Spyropoulos, 2002), grouping of users for music recommendation (Chen & Chen, 2001) based on complex amino acid sequences (Vernone, Berchialla, & Pescarmona, 2013). Such application exists also in the field of eye tracking, which is the focus of this paper.…”